Search Results (6,062)

Accurate sex identification in reptiles with genotypic sex determination is essential for breeding management, veterinary care and evolutionary research, yet commonly used methods are often invasive, stressful or unreliable. This study aimed to evaluate a dosage-based quantitative PCR approach for molecular sex determination in caenophidian snakes, using naturally shed epidermal skin as a non-invasive DNA source. Genomic DNA extracted from shed skin was analysed by qPCR targeting conserved Z-linked genes (ADARB2, ARMC4 and TANC2), together with autosomal and reference genes, to assess sex-specific differences in gene copy number. Sixteen caenophidian snake species were examined, including taxa for which molecular sexing data are currently scarce or unavailable. The autosomal control gene showed dosage ratios close to parity between sexes, supporting DNA quality and reference gene reliability; meanwhile, Z-linked markers generally exhibited reduced dosage in females relative to males, consistent with a ZZ/ZW sex determination system. These results demonstrate that dosage-based qPCR applied to shed epidermal skin provides a promising and non-invasive framework for molecular sex determination in caenophidian snakes, without compromising animal welfare. Full article

Automatic Identification System (AIS) data provide valuable insights into ship behavior, supporting maritime safety, situational awareness, and operational efficiency capabilities that are increasingly required for autonomous ship functions and harbor maneuvering assistance. This review synthesizes recent research on AIS-based ship behavior analysis and modeling published between 2022 and 2024 using a structured literature search and screening process informed by PRISMA principles. The review presents a five-stage workflow, spanning data processing, data analysis, knowledge extraction, modeling, and runtime applications with emphasis on how these stages contribute to perception, prediction, and decision support in automated navigation. Four dimensions are considered in data analysis, including statistical analysis, safety indicators, situational awareness, and anomaly detection. The modeling approaches are categorized into classification, regression, and optimization, highlighting current limitations such as data quality, algorithmic transparency, and real-time performance, while also assessing runtime feasibility for onboard or edge deployment. Three runtime application directions are identified: autonomous vessel functions, remote monitoring and control operations, and onboard decision-support tools, with numerous studies focusing on constrained waterways and port-approach scenarios. Future directions suggest integrating multi-source data and advancing machine learning models to improve robustness in complex traffic and harbor environments. By linking theoretical insights with practical onboard needs, this study provides guidance for developing intelligent, adaptive, and safety-enhancing maritime systems. Full article

The present study provides an exhaustive exploration of twelve macroalgal species from the Adriatic Sea, including seven brown algae (Ericaria amentacea, Fucus virsoides, Cutleria multifida, Cystoseira compressa, Cystoseira corniculata, Gongolaria barbata and Padina pavonica), three green algae (Codium adhaerens, Codium vermilara and Ulva lactuca), and two red algae (Scinaia furcellata and Asparagopsis taxiformis). Matrix solid-phase dispersion (MSPD) was applied as the extraction technique, using generally recognized as safe (GRAS) solvents. The bioactive profile of the extracts was assessed through the quantification of total phenolic content (TPC) and antioxidant activity. Among the three phyla, U. lactuca, F. virsoides and S. furcellata exhibited the highest TPC (0.8, 26 and 3.0 mgGAE·g⁻¹) and antioxidant activity (1.9, 38 and 7.5 mgTE·g⁻¹), respectively. Targeted HPLC-MS/MS analysis enabled the identification of nineteen phenolic compounds across all taxa. Chlorophyta showed a characteristic profile enriched in coumarins, benzaldehydes and flavanones, including the selective detection of 7-hydroxycoumarin in species with higher antioxidant potential. Additionally, compounds such as chlorogenic, rosmarinic and caffeic acids exhibited taxon-specific distributions that may explain differences in antioxidant activity. Complementary untargeted ultra-high performance liquid chromatography quadrupole time-of-flight (UHPLC-QToF) metabolomics analysis provided broader coverage, revealing eighty metabolites spanning phenolics, sugars, organic acids, lipids, amino acids and their derivatives. Notably, the proposed detection of fatty acid esters of hydroxy fatty acids (FAHFAs) represents the first report of these compounds in macroalgae, alongside a pronounced presence of sulphated phenolics. Overall, these findings provide a robust baseline on the bioactivity and chemical composition of Adriatic macroalgae, highlighting their value as a natural source of functional compounds. Full article

Partial discharge detection is a key technology for maintaining the normal operation of industrial power equipment. Oil-impregnated paper bushings are crucial components connecting transformers to the power grid. Insulation degradation leads to partial discharge, posing a significant threat to power system operation. Developing on-line diagnostics for partial discharge in transformer bushings and automatic identification of insulation defects can effectively protect system and personnel safety. Due to limitations of small sample sizes and lightweight networks, this study combines interpolation techniques with a lightweight convolutional neural network to improve identification accuracy. This network uses interpolation to maintain the undistorted sample signal from the initial input and reduces training defects from a small sample size. The neural network extracts partial discharge features to determine the defect type and its cause. This study uses a publicly available dataset with discharge signals from generators. Although from a different source from the discharge signals generated by oil-impregnated paper bushings, the signal distribution is similar, allowing for a fair analysis and providing a reference for evaluating discharge signals obtained from oil-impregnated paper bushings or other discharge devices. The experimental results show that the accuracy of this network improved from 97% to over 99% while maintaining low computational complexity and excellent real-time performance. Furthermore, this network was implemented and validated on existing industrial equipment. Full article

Groundwater quantification is essential for sustainable water resources management, yet it is often hampered by limited data availability and difficulties in measuring spring discharges. This study investigates three carbonate aquifers in Central Italy’s Abruzzo region: the Genzana–Greco, Morrone, and Marsicano mountains. The aim is to resolve uncertainties in spring attribution, and groundwater flow patterns using isotopic analyses combined with field surveys. The Genzana–Greco aquifer was examined to clarify the sources of the Acquachiara spring and the previously unreported Germina spring, assessing whether recharge occurs locally or from the carbonate massif. In this case, the results indicate that the Germina, together with a similar known spring of Capolaia, share a common recharge sector, while the Acquachiara spring is mainly fed by higher-elevation carbonate areas, excluding significant contributions from local alluvial deposits. In the Morrone mountain aquifer, discharge gains along the Pescara River through the Gole di Popoli were quantified, and spring isotopic compositions were compared to the main basal spring Giardino to better define groundwater contributions. In this case study, the stable isotopes and tritium data confirm recharge from the central–southern massif and support the identification of basal springs and Pescara River gains as primary discharge points, with minimal influence from surface water. For the Marsicano mountain aquifer, the role of Lake Scanno in feeding the Villalago springs was investigated through isotopic analysis of inflows, downstream springs, and basal aquifer discharge points to constrain the hydrogeological water budget. The results highlight Lake Scanno’s role in the recharge of Villalago springs and delineate the Cavuto group as a major discharge system receiving inputs from central and northern sectors of the massif. Overall, the integration of isotopic tracers with hydrological measurements allowed a more precise characterization of aquifer recharge areas, Mean Residence Times, and groundwater flow paths, improving the understanding of regional water resources in a complex carbonate setting. Full article

The integration of automated learning and video analysis enables the development of intelligent systems that can operate effectively in uncertain scenarios. These systems can autonomously identify dominant motion dynamics, depending on the theoretical framework used for representation and the learning process used for pattern identification. Current literature offers a state-based approach to describe the key temporal and spatial relationships required to understand motion dynamics. An important aspect of this approach is determining when the number of positively learned rules from a given information source is sufficient to detect dominant motion in automatic surveillance scenarios. This is crucial, as it affects both the variability of movements that monitored subjects can exhibit within the camera’s field of view and the resources needed for effective implementation. This study addresses these gaps through a grammar-based sufficiency criterion, which posits that learning is complete when production rule growth stabilizes, under the assumption of system stationarity. The stability criterion evaluates whether the most probable rules are learned over time, and whenever a high-growth rule is added, it is used to update the criterion. We outline several benefits of having a formal criterion for determining when a symbolic surveillance system has a robust model that explains the observed motion dynamics. Our hypothesis is that a correct model can consistently account for the majority of motion dynamics over time in an automated learning process. The proposed approach is evaluated by modeling motion dynamics in several scenarios using the SEQUITUR algorithm as input and computing the probability of stability along the learning curve, which indicates when the model reaches a steady state of consistent learning. Experimental validation was conducted in real-world scenarios under varying acquisition conditions. The results show that the proposed method achieves robust modeling performance, with accuracy values ranging from 83.56% to 95.92% in dynamic environments. Full article

Enterococci are widely used indicators of fecal contamination because they originate in the gastrointestinal tracts of warm-blooded animals, and species-level identification can support source attribution. This study evaluated the temporal abundance and species composition of enterococci in Quebrada Mondongo, southwestern Puerto Rico, a stream influenced by wastewater treatment plant (WWTP) effluent and nonpoint-source inputs. Five sampling campaigns for species distribution and fourteen for population quantification were conducted over approximately one year at the WWTP effluent discharge and at upstream and downstream stations. Enterococci concentrations exceeded the regulatory threshold for surface waters. Among the confirmed isolates, E. faecium dominated upstream and in the effluent, occurring approximately twofold more frequently than E. faecalis. Downstream, E. faecalis increased in relative abundance, shifting the species ratio of E. faecium/E. faecalis from 2.3–3.2 to 0.89. E. casseliflavus was detected at low frequency, and E. gallinarum was not observed. Virulence-associated genes (esp, gelE) were identified in ~75% of E. faecalis isolates, consistent with enhanced environmental persistence. Although upstream and effluent patterns reflected a strong human fecal signal, the downstream enrichment of E. faecalis suggests additional secondary inputs and/or naturalization. This study provides empirical evidence of species shifts in a tropical stream, with an increase in E. faecalis downstream of a WWTP despite E. faecium dominance in the effluent highlighting the likely influence of other nonpoint fecal sources within the watershed. Overall, these results suggest that the WWTP effluent did not contribute substantially to enterococci concentrations nor significantly influence the species composition of enterococci downstream in Quebrada Mondongo, highlighting the likely influence of other nonpoint fecal sources within the watershed. Full article

Non-typhoidal Salmonella (NTS) is an important poultry-associated pathogen with major One Health and economic impacts, but data on its epidemiology and antimicrobial resistance in Nigeria remain limited. This study investigated the prevalence, serovar distribution, clonal relatedness, and antimicrobial resistance of NTS along the poultry production chain in Enugu State, southeastern Nigeria. A total of 2400 samples were collected, comprising feces (cecal content)/cloacal swabs from chickens (n = 1100), eggs (n = 400), chicken meat (n = 600), and stool samples from poultry workers (n = 300). Isolation and identification were performed using standard bacteriological methods, with confirmation by serotyping and polymerase chain reaction (PCR) targeting the invA gene. Genetic relatedness was assessed using enterobacterial repetitive intergenic consensus (ERIC)-PCR, and antimicrobial susceptibility was determined by the disk diffusion method. Overall, 47 (2.0%) Salmonella enterica isolates were recovered from 2400 samples, with the highest prevalence observed in eggs (3.5%), followed by human stool (3.3%), chicken meat (1.8%), and chicken feces (1.1%). Only 35 (11.8%) of the 297 sampled farms were positive for Salmonella, and recovery rates differed significantly (p = 0.0065) among sample sources. Five serotypes were identified, dominated by S. Typhimurium (57.4%), followed by S. Enteritidis (14.9%), S. Anatum (12.8%), S. Stanley (8.5%), and S. Agona (6.3%). ERIC-PCR revealed multiple clonal clusters, many containing isolates from mixed sources, indicating circulation of related strains between poultry and humans. All isolates were resistant to ampicillin, with high resistance to tetracycline (76.6%), sulphamethoxazole–trimethoprim (51.1%), and fluoroquinolones. Overall, 80.9% of isolates were multidrug-resistant, with a mean Multiple Antibiotic Resistance Index of 0.29, highest among isolates from chicken feces. Although the prevalence of NTS was low, the presence of genetically related multidrug-resistant strains across the production chain underscores the role of poultry as a reservoir for zoonotic transmission and highlights the need for coordinated One Health surveillance and antimicrobial stewardship strategies in Nigeria. Full article

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault detection sensitivity across various rotational speeds and load conditions. Using an accelerometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphone positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), kurtosis and Crest Factor (CF) values, with advanced diagnostic techniques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spectrum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, kurtosis, CF) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the maximum sensor distance. The study concludes that optimal microphone placement is essential for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications. Full article

The large-scale integration and grid connection of renewable energy sources and charging stations introduce a multitude of nonlinear and impact loads, resulting in more severe distortion and higher complexity of disturbance signals in power systems. As a consequence, power quality disturbances (PQDs) in active distribution networks, including overvoltage and harmonics, display greater randomness and diversity, which increases the challenge of PQD identification. To tackle this problem, this study presents a dual-channel early-fusion approach for PQD recognition based on Spatial Post-MultiScale Fusion Entropy (SMFE). SMFE is used as an entropy-based feature-construction pipeline in which a time–frequency representation is formed prior to spatial post-multiscale aggregation to produce a compact complexity map complementary to waveform morphology. Subsequently, a dual-channel model is constructed by integrating waveform-morphology input with SMFE-derived complexity features for joint learning. By leveraging the ConvNeXt architecture and a Squeeze-and-Excitation (SE) mechanism, a multimodal channel-recalibration model is implemented to emphasize informative feature responses during PQD recognition. Experimental verification with simulated signals shows that the proposed approach achieves an identification accuracy of 97.83% under an SNR of 30 dB, indicating robust performance under the tested noise settings. Full article

Position-independent geometric errors crucially impact the accuracy of five-axis machine tools, yet their identification remains challenging due to computational complexities, inadequate measurement pose selection, and disturbances arising from thermal drift and residual uncompensated errors. Existing methods typically rely on linearized kinematic models, heuristic sampling of measurement poses, or computationally expensive global optimization procedures, which collectively limit their effectiveness in industrial environments. This study presents a unified identification framework that overcomes these limitations; it incorporates 3D offset parameters to enhance the decoupling of true geometric errors from non-PIGEs, an observability-driven measurement pose selection strategy to maximize the parameter sensitivity, and an ANN-surrogate model to accelerate high-dimensional global optimization. A genetic algorithm is used to optimize the measurement points based on the observability index of the machine tool. The ANN-surrogate model enhances the identification accuracy of error parameters (11 PIGEs + 3 offsets) through precise kinematic models, global exploration, and final refinement. Experimental validation on a five-axis machine tool demonstrates a volumetric error reduction of 88.615% after compensation, with RMSE decreasing to 0.4337 μm. Sensitivity analysis reveals that PIGEs contribute up to 75.26% of the total inaccuracy, while offset parameters capture 24.74% of the error from thermal and non-PIGE sources. The results confirm the method’s superiority over other techniques in terms of identification accuracy, efficiency, and robustness, providing a practical solution for high-precision applications in the manufacturing industries. Full article

Renewable energy sources, and wind energy in particular, constitute a central pillar of energy policy at both national and European levels. Nevertheless, the deployment of onshore wind farms is frequently associated with spatial, environmental, and social conflicts, making the evaluation of existing projects imperative. The present study aimed to assess the sustainability of existing onshore wind farms in the Region of Thessaly, with particular emphasis on their spatial planning, technical characteristics, and environmental impacts. The methodological framework consists of four distinct stages: (i) identification and spatial mapping of existing wind farms in the study area, (ii) assessment of the compliance of existing wind installations with the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD–RES), (iii) application of the Rapid Impact Assessment Matrix (RIAM) to enable a systematic and comparable evaluation of the impacts of wind installations on specific environmental and anthropogenic parameters, and (iv) estimation of project hazard and operational vulnerability through the application of Operational Risk Management (ORM). Geographic Information Systems (GISs) were employed for data processing and spatial analysis. The assessment showed that 40% of the evaluated wind farms fully comply with all eleven exclusion criteria of the SFSPSD-RES, whereas the remaining 60% show partial compliance, failing to meet between one and three criteria. RIAM results indicate that the most significant adverse impacts (−D and −C) during construction are associated with morphology/soils and the natural environment, mainly due to loss/fragmentation of vegetation and disturbance of fauna, and, in some cases, in areas of increased sensitivity. During operation, the main negative effects (−D and −C) relate to landscape and visual quality, as well as continued disturbance to the natural environment. At the same time, the operation generates important positive effects (+E) on the atmospheric environment through reduced CO₂ emissions. The ORM analysis further shows that the most important risks for most wind farms arise during construction (ORM = 2 and 3), particularly from serious worker accidents during lifting, roadworks, and foundation activities. The study demonstrates that the sustainability of existing wind installations depends on a complex set of spatial, environmental, and technical factors. The proposed framework integrates spatial compliance screening, RIAM-based environmental impact assessment, and ORM-based risk and opportunity evaluation. This connection links the importance of impacts with their operational manageability during construction and operation phases, as well as across sustainability dimensions. Consequently, the study provides a more decision-focused approach for assessing existing wind farms and supporting policy development. Full article

Anti-analysis techniques, also known as evasive techniques, enable Windows malware to detect and evade dynamic inspection environments, undermining the effectiveness of virtual-machine and sandbox-based inspection. Despite extensive prior research, no unified classification has been paired with a large-scale empirical evaluation of static detection capabilities for these behaviors. This paper addresses this gap by presenting a comprehensive classification and detection framework. We consolidate 94 anti-analysis techniques from academic, community, and threat-intelligence sources into nine mechanistic categories and derive corresponding YARA rules for static identification. In total, 82 YARA signatures were authored or refined and evaluated on 459,508 malware and 92,508 goodware samples. After iterative refinement using precision thresholds, 42 rules achieved high accuracy (≥75%), 16 showed moderate precision (50–75%), and 24 were discarded due to unreliability. The results indicate strong static detectability for firmware- and BIOS-based checks, but limited precision for timing-based evasions, which frequently overlap with benign behavior. Although YARA provides broad coverage of observable artifacts, its static nature limits detection under obfuscation or runtime mutation; our measurements therefore represent conservative estimates of technique prevalence. All validated rules are released in an open-source repository to support reproducibility, improve incident-response workflows, and strengthen prevention and mitigation against real-world threats. Future work will explore hybrid validation, container-evasion extensions, and forensic attribution based on signature co-occurrence patterns. Full article

Rapid expansion of electric vehicle adoption has led to increased dependence on a charging infrastructure that is tightly integrated with energy distribution systems and digital communication networks. As electric vehicle charging stations evolve into complex cyber–physical systems, cybersecurity risks pose a growing threat to grid reliability and user trust. This paper presents a hybrid decision-support framework for cybersecurity risk assessment in EV charging infrastructure that advances beyond prior multi-criteria decision-making approaches by combining interpretability with data-driven validation. Specifically, the framework integrates the Analytic Hierarchy Process (AHP) for expert-driven weighting of cybersecurity attributes with PROMETHEE for flexible threat prioritization, enabling transparent and auditable risk rankings. The framework categorizes cybersecurity criteria across four infrastructure layers—transmission, distribution, consumer, and electric vehicle charging stations—and assigns relative weights through expert-driven pairwise comparisons. PROMETHEE is then applied to rank potential cyber threats based on these weights, allowing for flexible prioritization of cybersecurity interventions. The methodology is validated using the real-world WUSTL-IIoT-2018 SCADA dataset, which includes simulated reconnaissance (network scanning), device identification, and exploitation attacks. While this dataset does not natively include OCPP 2.0 or ISO 15118 protocols, the experimental results demonstrate strong discrimination power (AUC = 0.99, recall = 95%) and provide a basis for extension to modern EVSE communication standards. The results identify critical metrics such as anomalous source packet behavior and encryption reliability as key vulnerability markers, aligning with documented EV charging attack scenarios. By bridging expert judgment with empirical traffic data, the proposed framework offers both technical robustness and explainability, supporting grid operators, SOC teams, and infrastructure planners in systematically assessing risks, allocating resources, and enhancing the resilience of EV charging ecosystems against evolving cyber threats. Full article

Transcriptome profiling is a cornerstone of functional genomics, enabling the detailed characterization of gene expression in health and disease. Bulk RNA sequencing (bulk RNAseq) remains the most widely used approach in clinical and large-cohort studies due to its cost-effectiveness, robustness, and comprehensive transcriptome coverage. However, bulk RNAseq inherently averages gene expression signals across heterogeneous cell populations, thereby masking cellular diversity and obscuring rare cell types. In contrast, single-cell RNA sequencing (scRNAseq) enables a high-resolution analysis of cellular heterogeneity, allowing the identification of distinct cell types, transitional states, and developmental trajectories. Nevertheless, scRNAseq is associated with higher cost, limited scalability, increased technical noise, sparse expression matrices, and protocol-dependent biases introduced during tissue dissociation or nuclear isolation. In this review, we summarize the conceptual and methodological foundations of integrating bulk RNAseq and scRNAseq data, emphasizing their complementary strengths and limitations. We discuss how scRNAseq-derived cell-type atlases can serve as reference matrices for computational reconstruction (deconvolution) of bulk RNAseq profiles and examine key sources of technical and biological variability. Furthermore, we outline major integration strategies, including reference-based deconvolution, pseudobulk aggregation, and Bayesian joint modeling to provide an overview of widely used analytical tools and essential components of scRNAseq data processing workflows. Full article